Instructions to use Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context with Transformers:

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

pipe = pipeline("text-generation", model="Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context", trust_remote_code=True)
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 Settings

vLLM

How to use Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context

SGLang

How to use Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context 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 "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context" \
    --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": "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
		"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 "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context" \
        --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": "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context with Docker Model Runner:
```
docker model run hf.co/Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fett-uccine-Long-Noodle-7B-120k-Context

This is a merge of pre-trained language models created using mergekit.

Merge Details

A merge with Fett-uccine and Mistral Yarn 120k ctx.

Credit to Nitral for the merge script and idea.

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Z:\ModelColdStorage\Yarn-Mistral-7b-128k
Z:\ModelColdStorage\Fett-uccine-7B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Z:\ModelColdStorage\Fett-uccine-7B
        layer_range: [0, 32]
      - model: Z:\ModelColdStorage\Yarn-Mistral-7b-128k
        layer_range: [0, 32]
merge_method: slerp
base_model: Z:\ModelColdStorage\Fett-uccine-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Downloads last month: 10

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context

Merges

8 models

Quantizations

2 models

Collection including Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context

Fett-uccine

Collection

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