Not-For-All-Audiences

Instructions to use tannedbum/L3-Nymeria-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tannedbum/L3-Nymeria-8B with Transformers:

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

pipe = pipeline("text-generation", model="tannedbum/L3-Nymeria-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tannedbum/L3-Nymeria-8B")
model = AutoModelForCausalLM.from_pretrained("tannedbum/L3-Nymeria-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tannedbum/L3-Nymeria-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tannedbum/L3-Nymeria-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tannedbum/L3-Nymeria-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tannedbum/L3-Nymeria-8B

SGLang

How to use tannedbum/L3-Nymeria-8B 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 "tannedbum/L3-Nymeria-8B" \
    --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": "tannedbum/L3-Nymeria-8B",
		"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 "tannedbum/L3-Nymeria-8B" \
        --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": "tannedbum/L3-Nymeria-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tannedbum/L3-Nymeria-8B with Docker Model Runner:
```
docker model run hf.co/tannedbum/L3-Nymeria-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The smartest L3 8B model combined with high-end RP model. What could go wrong.

The idea was to fuse a bit of SimPO's realism with Stheno. It took a few days to come up with a balanced slerp configuration, but I'm more than satisfied with the end result.

SillyTavern

Text Completion presets

temp 0.9
top_k 30
top_p 0.75
min_p 0.2
rep_pen 1.1
smooth_factor 0.25
smooth_curve 1

Advanced Formatting

Context & Instruct preset by Virt-io

Instruct Mode: Enabled

merge

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

This model was merged using the slerp merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


slices:
  - sources:
      - model: Sao10K/L3-8B-Stheno-v3.2
        layer_range: [0, 32]
      - model: princeton-nlp/Llama-3-Instruct-8B-SimPO
        layer_range: [0, 32]
merge_method: slerp
base_model: Sao10K/L3-8B-Stheno-v3.2
parameters:
  t:
    - filter: self_attn
      value: [0.4, 0.5, 0.6, 0.4, 0.6]
    - filter: mlp
      value: [0.6, 0.5, 0.4, 0.6, 0.4]
    - value: 0.5
dtype: bfloat16

Original model information:

Model: Sao10K/L3-8B-Stheno-v3.2

Stheno-v3.2-Zeta

Changes compared to v3.1
- Included a mix of SFW and NSFW Storywriting Data, thanks to Gryphe
- Included More Instruct / Assistant-Style Data
- Further cleaned up Roleplaying Samples from c2 Logs -> A few terrible, really bad samples escaped heavy filtering. Manual pass fixed it.
- Hyperparameter tinkering for training, resulting in lower loss levels.

Testing Notes - Compared to v3.1
- Handles SFW / NSFW seperately better. Not as overly excessive with NSFW now. Kinda balanced.
- Better at Storywriting / Narration.
- Better at Assistant-type Tasks.
- Better Multi-Turn Coherency -> Reduced Issues?
- Slightly less creative? A worthy tradeoff. Still creative.
- Better prompt / instruction adherence.

Want to support my work ? My Ko-fi page: https://ko-fi.com/tannedbum

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for tannedbum/L3-Nymeria-8B

Sao10K/L3-8B-Stheno-v3.2

princeton-nlp/Llama-3-Instruct-8B-SimPO

Merge model

this model

Merges

9 models

Quantizations