hamel/hc-mistral-qlora-6

Model Description

A model that can generate Honeycomb Queries.

fine-tuned by Hamel Husain

How to Get Started with the Model

Make sure you install all dependencies

pip install transformers==transformers==4.36.2 datasets==2.15.0 peft==0.6.0 accelerate==0.24.1 bitsandbytes==0.41.3.post2 safetensors==0.4.1 scipy==1.11.4 sentencepiece==0.1.99 protobuf==4.23.4 --upgrade

Next, load the dependencies.

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model_id='hamel/hc-mistral-qlora-6'
model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

Next, define a function that can help you with the prompt (alpaca style). It is important that you follow this prompt exactly, because this is how the model was trained.

def prompt(nlq, cols):
    return f"""[INST] <<SYS>>
Honeycomb AI suggests queries based on user input and candidate columns.
<</SYS>>

User Input: {nlq}

Candidate Columns: {cols}
[/INST]
"""

def prompt_tok(nlq, cols):
    _p = prompt(nlq, cols)
    input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
    out_ids = model.generate(input_ids=input_ids, max_new_tokens=5000, 
                          do_sample=False)
    return tokenizer.batch_decode(out_ids.detach().cpu().numpy(), 
                                  skip_special_tokens=True)[0][len(_p):]

Next, make predictions

nlq = "Exception count by exception and caller"
cols = ['error', 'exception.message', 'exception.type', 'exception.stacktrace', 'SampleRate', 'name', 'db.user', 'type', 'duration_ms', 'db.name', 'service.name', 'http.method', 'db.system', 'status_code', 'db.operation', 'library.name', 'process.pid', 'net.transport', 'messaging.system', 'rpc.system', 'http.target', 'db.statement', 'library.version', 'status_message', 'parent_name', 'aws.region', 'process.command', 'rpc.method', 'span.kind', 'serializer.name', 'net.peer.name', 'rpc.service', 'http.scheme', 'process.runtime.name', 'serializer.format', 'serializer.renderer', 'net.peer.port', 'process.runtime.version', 'http.status_code', 'telemetry.sdk.language', 'trace.parent_id', 'process.runtime.description', 'span.num_events', 'messaging.destination', 'net.peer.ip', 'trace.trace_id', 'telemetry.instrumentation_library', 'trace.span_id', 'span.num_links', 'meta.signal_type', 'http.route']

out = prompt_tok(nlq, cols)
print(out)

Training Details

See this wandb run

Training Data

~90k synthetically generated honeycomb queries. This data is located in alpaca_synth_queries.jsonl.

Training Procedure

Used axolotl, see this config.