Instructions to use neural-coder/gorilla-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neural-coder/gorilla-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="neural-coder/gorilla-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neural-coder/gorilla-finetuned")
model = AutoModelForCausalLM.from_pretrained("neural-coder/gorilla-finetuned")
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]:]))

PEFT
How to use neural-coder/gorilla-finetuned with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use neural-coder/gorilla-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/neural-coder/gorilla-finetuned

SGLang

How to use neural-coder/gorilla-finetuned 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 "neural-coder/gorilla-finetuned" \
    --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": "neural-coder/gorilla-finetuned",
		"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 "neural-coder/gorilla-finetuned" \
        --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": "neural-coder/gorilla-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neural-coder/gorilla-finetuned with Docker Model Runner:
```
docker model run hf.co/neural-coder/gorilla-finetuned
```

gorilla-finetuned / training_params.json

neural-coder

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	{
	"model": "gorilla-llm/gorilla-openfunctions-v2",
	"project_name": "gorilla-finetuned",
	"data_path": "gorilla-finetuned/autotrain-data",
	"train_split": "train",
	"valid_split": null,
	"add_eos_token": true,
	"block_size": 2048,
	"model_max_length": 8091,
	"padding": "right",
	"trainer": "sft",
	"use_flash_attention_2": false,
	"log": "wandb",
	"disable_gradient_checkpointing": false,
	"logging_steps": 5,
	"eval_strategy": "epoch",
	"save_total_limit": 3,
	"auto_find_batch_size": false,
	"mixed_precision": "bf16",
	"lr": 5e-06,
	"epochs": 10,
	"batch_size": 4,
	"warmup_ratio": 0.1,
	"gradient_accumulation": 16,
	"optimizer": "adamw_torch",
	"scheduler": "cosine_with_restarts",
	"weight_decay": 0.01,
	"max_grad_norm": 0.3,
	"seed": 42,
	"chat_template": null,
	"quantization": "int8",
	"target_modules": "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj",
	"merge_adapter": true,
	"peft": true,
	"lora_r": 64,
	"lora_alpha": 128,
	"lora_dropout": 0.05,
	"model_ref": null,
	"dpo_beta": 0.1,
	"max_prompt_length": 2500,
	"max_completion_length": null,
	"prompt_text_column": "autotrain_prompt",
	"text_column": "autotrain_text",
	"rejected_text_column": "autotrain_rejected_text",
	"push_to_hub": true,
	"username": "neural-coder",
	"unsloth": false,
	"distributed_backend": null
	}