Upload LlamaForCausalLM

README.md

@@ -1,428 +1,199 @@
 ---
-language:
-- en
-- de
-- fr
-- it
-- pt
-- hi
-- es
-- th
 library_name: transformers
-pipeline_tag: text-generation
-tags:
-- facebook
-- meta
-- pytorch
-- llama
-- llama-3
-license: llama3.2
-extra_gated_prompt: >-
-  ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
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-  
-  ### Llama 3.2 Acceptable Use Policy
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-  #### Prohibited Uses
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-  * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)
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-  * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
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+tags: []
 ---
 
-## Model Information
+# Model Card for Model ID
 
-The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
+<!-- Provide a quick summary of what the model is/does. -->
 
-**Model Developer:** Meta
 
-**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. 
 
-|  | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
-| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
-| Llama 3.2 (text only)  | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code  | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
-|  |  | 3B (3.21B) | Multilingual Text | Multilingual Text and code  |  |  |  |  |  |
+## Model Details
 
-**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
+### Model Description
 
-**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
+<!-- Provide a longer summary of what this model is. -->
 
-**Model Release Date:** Sept 25, 2024
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. 
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
+### Model Sources [optional]
 
-**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 
+<!-- Provide the basic links for the model. -->
 
-## Intended Use
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. 
+## Uses
 
-**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-## How to use
+### Direct Use
 
-This repository contains two versions of Llama-3.2-1B-Instruct, for use with `transformers` and with the original `llama` codebase.
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-### Use with transformers
+[More Information Needed]
 
-Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+### Downstream Use [optional]
 
-Make sure to update your transformers installation via `pip install --upgrade transformers`.
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-```python
-import torch
-from transformers import pipeline
+[More Information Needed]
 
-model_id = "meta-llama/Llama-3.2-1B-Instruct"
-pipe = pipeline(
-    "text-generation",
-    model=model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Who are you?"},
-]
-outputs = pipe(
-    messages,
-    max_new_tokens=256,
-)
-print(outputs[0]["generated_text"][-1])
-```
+### Out-of-Scope Use
 
-Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-### Use with `llama`
+[More Information Needed]
 
-Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
+## Bias, Risks, and Limitations
 
-To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-```
-huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
-```
+[More Information Needed]
 
-## Hardware and Software
+### Recommendations
 
-**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## 
+## How to Get Started with the Model
 
-**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
+Use the code below to get started with the model.
 
-|  | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
-| :---- | :---: | ----- | :---: | :---: | :---: |
-| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
-| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
-| Total | 830k |         86k |  | 240 | 0 |
+[More Information Needed]
 
-The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
+## Training Details
 
-## Training Data
+### Training Data
 
-**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-**Data Freshness:** The pretraining data has a cutoff of December 2023\.
+[More Information Needed]
 
-## Benchmarks \- English Text
+### Training Procedure
 
-In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. 
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-### Base Pretrained Models 
+#### Preprocessing [optional]
 
-| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
-| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
-| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
-|  | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
-|  | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
-| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
-|  | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
-|  | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
-| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
+[More Information Needed]
 
-### Instruction Tuned Models
 
-| Capability |  | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
-| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: |
-| General |  | MMLU | 5 | macro\_avg/acc | 49.3 | 63.4 | 69.4 |
-| Re-writing |  | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 40.1 | 40.9 |
-| Summarization |  | TLDR9+ (test) | 1 | rougeL | 16.8 | 19.0 | 17.2 |
-| Instruction following |  | IFEval | 0 | avg(prompt/instruction acc loose/strict) | 59.5 | 77.4 | 80.4 |
-| Math |  | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 77.7 | 84.5 |
-|  |  | MATH (CoT) | 0 | final\_em | 30.6 | 47.3 | 51.9 |
-| Reasoning |  | ARC-C | 0 | acc | 59.4 | 78.6 | 83.4 |
-|  |  | GPQA | 0 | acc | 27.2 | 32.8 | 32.8 |
-|  |  | Hellaswag | 0 | acc | 41.2 | 69.8 | 78.7 |
-| Tool Use |  | BFCL V2 | 0 | acc | 25.7 | 67.0 | 70.9 |
-|  |  | Nexus | 0 | macro\_avg/acc | 13.5 | 34.3 | 38.5 |
-| Long Context |  | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | 19.8 | 27.3 |
-|  |  | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | 63.3 | 72.2 |
-|  |  | NIH/Multi-needle | 0 | recall | 75.0 | 84.7 | 98.8 |
-| Multilingual |  | MGSM (CoT) | 0 | em | 24.5 | 58.2 | 68.9 |
+#### Training Hyperparameters
 
-### Multilingual Benchmarks
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
-| :---: | :---: | :---: | :---: | :---: | :---: |
-| General | MMLU (5-shot, macro\_avg/acc) | Portuguese | 39.82 | 54.48 | 62.12 |
-|  |  | Spanish | 41.5 | 55.1 | 62.5 |
-|  |  | Italian | 39.8 | 53.8 | 61.6 |
-|  |  | German | 39.2 | 53.3 | 60.6 |
-|  |  | French | 40.5 | 54.6 | 62.3 |
-|  |  | Hindi | 33.5 | 43.3 | 50.9 |
-|  |  | Thai | 34.7 | 44.5 | 50.3 |
+#### Speeds, Sizes, Times [optional]
 
-## Responsibility & Safety
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
+[More Information Needed]
 
-1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama   
-2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm  
-3. Provide protections for the community to help prevent the misuse of our models
+## Evaluation
 
-### Responsible Deployment 
+<!-- This section describes the evaluation protocols and provides the results. -->
 
-**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). 
+### Testing Data, Factors & Metrics
 
-#### Llama 3.2 Instruct 
+#### Testing Data
 
-**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). 
+<!-- This should link to a Dataset Card if possible. -->
 
-**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. 
+[More Information Needed]
 
-**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. 
+#### Factors
 
-#### Llama 3.2 Systems
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. 
+[More Information Needed]
 
-### New Capabilities and Use Cases
+#### Metrics
 
-**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. 
+[More Information Needed]
 
-### Evaluations
+### Results
 
-**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
+[More Information Needed]
 
-**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
+#### Summary
 
-### Critical Risks 
 
-In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
 
-**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 
+## Model Examination [optional]
 
-**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 
+<!-- Relevant interpretability work for the model goes here -->
 
-**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
-Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
+[More Information Needed]
 
-### Community 
+## Environmental Impact
 
-**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
 
-## Ethical Considerations and Limitations
+## Technical Specifications [optional]
 
-**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. 
+### Model Architecture and Objective
 
-**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.   
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "meta-llama/Llama-3.2-1B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.45.0"
+}

model.safetensors

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+size 2471645608