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]:]))

Inference
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
```

dty1aaa commited on Mar 30

Commit

6334fd3

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1 Parent(s): 575c082

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+---
+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: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# final_sft_CodeLlama-7b-Instruct-hf
+This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the efficoder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3331
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## 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