Instructions to use dty1aaa/codellama-7b-instruct-hf-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dty1aaa/codellama-7b-instruct-hf-sft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dty1aaa/codellama-7b-instruct-hf-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dty1aaa/codellama-7b-instruct-hf-sft")
model = AutoModelForCausalLM.from_pretrained("dty1aaa/codellama-7b-instruct-hf-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dty1aaa/codellama-7b-instruct-hf-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dty1aaa/codellama-7b-instruct-hf-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dty1aaa/codellama-7b-instruct-hf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dty1aaa/codellama-7b-instruct-hf-sft

SGLang

How to use dty1aaa/codellama-7b-instruct-hf-sft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dty1aaa/codellama-7b-instruct-hf-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dty1aaa/codellama-7b-instruct-hf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dty1aaa/codellama-7b-instruct-hf-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dty1aaa/codellama-7b-instruct-hf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dty1aaa/codellama-7b-instruct-hf-sft with Docker Model Runner:
```
docker model run hf.co/dty1aaa/codellama-7b-instruct-hf-sft
```

codellama-7b-instruct-hf-sft / README.md

dty1aaa

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d4b8df7 verified about 2 months ago

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metadata

library_name: transformers
license: other
base_model: codellama/CodeLlama-7b-Instruct-hf
tags:
  - llama-factory
  - full
  - generated_from_trainer
model-index:
  - name: final_sft_CodeLlama-7b-Instruct-hf
    results: []

final_sft_CodeLlama-7b-Instruct-hf

This model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-hf on the efficoder dataset. It achieves the following results on the evaluation set:

Loss: 0.3331

Model description

train on EFFIINSTRUCT_python

Intended uses & limitations

generate effective code

Training and evaluation data

train on EFFIINSTRUCT_python

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 8
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 4.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.2475	0.3401	50	0.2433
0.2425	0.6803	100	0.2314
0.2147	1.0204	150	0.2318
0.1702	1.3605	200	0.2378
0.1758	1.7007	250	0.2396
0.1438	2.0408	300	0.2643
0.0989	2.3810	350	0.2823
0.0991	2.7211	400	0.2799
0.065	3.0612	450	0.3123
0.0601	3.4014	500	0.3348
0.0645	3.7415	550	0.3324

Framework versions

Transformers 4.44.2
Pytorch 2.2.2+cu121
Datasets 4.8.4
Tokenizers 0.19.1