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

308ade9

verified ·

1 Parent(s): 6908cc5

Update README.md

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@@ -37,10 +37,11 @@ This model has been fine-tuned on [Egrigor/ValheimTestData](https://huggingface.
 To load and interact with the model, use the following code:
-from transformers import AutoModelForCausalLM, AutoTokenizer
-# Load the model and tokenizer
-model = AutoModelForCausalLM.from_pretrained("Egrigor/ValheimAssistantV1")
 tokenizer = AutoTokenizer.from_pretrained("Egrigor/ValheimAssistantV1")
 # Prepare input and generate output

 To load and interact with the model, use the following code:
+from unsloth import FastLanguageModel
+from transformers import AutoTokenizer
+# Load model and tokenizer
+model = FastLanguageModel.from_pretrained("Egrigor/ValheimAssistantV1")
 tokenizer = AutoTokenizer.from_pretrained("Egrigor/ValheimAssistantV1")
 # Prepare input and generate output