Instructions to use kolerk/tcod_7b_b2f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kolerk/tcod_7b_b2f with Transformers:

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

pipe = pipeline("text-generation", model="kolerk/tcod_7b_b2f")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kolerk/tcod_7b_b2f")
model = AutoModelForCausalLM.from_pretrained("kolerk/tcod_7b_b2f")
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 kolerk/tcod_7b_b2f with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kolerk/tcod_7b_b2f"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kolerk/tcod_7b_b2f",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kolerk/tcod_7b_b2f

SGLang

How to use kolerk/tcod_7b_b2f 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 "kolerk/tcod_7b_b2f" \
    --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": "kolerk/tcod_7b_b2f",
		"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 "kolerk/tcod_7b_b2f" \
        --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": "kolerk/tcod_7b_b2f",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kolerk/tcod_7b_b2f with Docker Model Runner:
```
docker model run hf.co/kolerk/tcod_7b_b2f
```

tcod_7b_b2f / README.md

kolerk

Update README for ALFWorld model description

065c559 verified 10 days ago

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- text-generation
	- ALFWorld
	---
	# tcod_7b_b2f for ALFWorld

	This model is for ALFWorld.

	## Download

	Use Hugging Face Hub:

	```python
	from huggingface_hub import snapshot_download

	model_dir = snapshot_download("kolerk/tcod_7b_b2f")
	print(model_dir)
	```

	Or clone with Git LFS:

	```bash
	git lfs install
	git clone https://huggingface.co/kolerk/tcod_7b_b2f
	```