Instructions to use Egrigor/ValheimAssistantV1-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Egrigor/ValheimAssistantV1-16bit with Transformers:

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

pipe = pipeline("text-generation", model="Egrigor/ValheimAssistantV1-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Egrigor/ValheimAssistantV1-16bit")
model = AutoModelForCausalLM.from_pretrained("Egrigor/ValheimAssistantV1-16bit", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Egrigor/ValheimAssistantV1-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Egrigor/ValheimAssistantV1-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Egrigor/ValheimAssistantV1-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Egrigor/ValheimAssistantV1-16bit

SGLang

How to use Egrigor/ValheimAssistantV1-16bit 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 "Egrigor/ValheimAssistantV1-16bit" \
    --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": "Egrigor/ValheimAssistantV1-16bit",
		"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 "Egrigor/ValheimAssistantV1-16bit" \
        --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": "Egrigor/ValheimAssistantV1-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Egrigor/ValheimAssistantV1-16bit with Docker Model Runner:
```
docker model run hf.co/Egrigor/ValheimAssistantV1-16bit
```

Egrigor commited on Jan 23, 2025

Commit

17cbb36

verified ·

1 Parent(s): df0faf5

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -48,7 +48,7 @@ outputs = model.generate(**inputs, max_length=100)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-Training Details
@@ -65,14 +65,14 @@ Total Training Steps: 60
 Training Time: ~3.5 minutes
-Dataset
 The dataset consists of gameplay tips, lore insights, and strategic guidance, formatted into conversational question-and-answer pairs.
 Note: As this dataset was semi-automatically generated from community resources, it may contain inaccuracies or gaps. Feedback is welcome for future improvements.
-Limitations and Future Work
 Dataset Gaps: The dataset may not fully cover Valheims gameplay and lore, and some responses may be incomplete or inaccurate.
@@ -81,7 +81,7 @@ Generalization: This model is focused solely on Valheim-related content and may
 Iterative Improvement: Future versions will refine the dataset and model based on testing and community feedback.
-Contact and Feedback
 This is an experimental project created by a novice AI enthusiast. Feedback, suggestions, and collaborations are highly welcome! Feel free to reach out via Hugging Face.

 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+## Training Details
 Training Time: ~3.5 minutes
+## Dataset
 The dataset consists of gameplay tips, lore insights, and strategic guidance, formatted into conversational question-and-answer pairs.
 Note: As this dataset was semi-automatically generated from community resources, it may contain inaccuracies or gaps. Feedback is welcome for future improvements.
+## Limitations and Future Work
 Dataset Gaps: The dataset may not fully cover Valheims gameplay and lore, and some responses may be incomplete or inaccurate.
 Iterative Improvement: Future versions will refine the dataset and model based on testing and community feedback.
+## Contact and Feedback
 This is an experimental project created by a novice AI enthusiast. Feedback, suggestions, and collaborations are highly welcome! Feel free to reach out via Hugging Face.