Instructions to use sometimesanotion/Lamarck-14B-v0.7-Fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sometimesanotion/Lamarck-14B-v0.7-Fusion with Transformers:

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

pipe = pipeline("text-generation", model="sometimesanotion/Lamarck-14B-v0.7-Fusion")
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("sometimesanotion/Lamarck-14B-v0.7-Fusion")
model = AutoModelForCausalLM.from_pretrained("sometimesanotion/Lamarck-14B-v0.7-Fusion", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sometimesanotion/Lamarck-14B-v0.7-Fusion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sometimesanotion/Lamarck-14B-v0.7-Fusion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sometimesanotion/Lamarck-14B-v0.7-Fusion",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sometimesanotion/Lamarck-14B-v0.7-Fusion

SGLang

How to use sometimesanotion/Lamarck-14B-v0.7-Fusion 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 "sometimesanotion/Lamarck-14B-v0.7-Fusion" \
    --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": "sometimesanotion/Lamarck-14B-v0.7-Fusion",
		"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 "sometimesanotion/Lamarck-14B-v0.7-Fusion" \
        --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": "sometimesanotion/Lamarck-14B-v0.7-Fusion",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sometimesanotion/Lamarck-14B-v0.7-Fusion with Docker Model Runner:
```
docker model run hf.co/sometimesanotion/Lamarck-14B-v0.7-Fusion
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EXPERIMENTAL:

Evaluations show high GPQA and strong reasoning capabilities in addition to this model's fine prose! IFEVAL is modest compared to other capabilities. This model is best used for free-form creativity.

So what's this new arcee_fusion merge method, and what can we do with it? This model aims to find out, as a multi-stage merge where 3 out of 4 steps are fusions:

A fusion of Lamarck-14B-v0.7 and @suayptalha's Lamarckvergence SLERP merge of Lamarck-14B-v0.7 and Qwenvergence-14B-v12-Prose-DS.
A SLERP of Lamarck-14B-v0.7-Fusionvergence with Qwenvergence-14B-v12-Prose-DS, the latter emphasized in later layers.
A fusion of @jpacifico's Chocolatine-2-14B-Instruct-v2.0.3, itself a finetune of a merge of Lamarck-14B-v0.7, Arcee's (https://huggingface.co/arcee-ai/Virtuoso-Small-v2), and Qwenvergence-14B-v12-Prose-DS, fusion-merged with - you guessed it - Qwenvergence-14B-v12-Prose-DS
A fusion of the previous two.

I've seen strong prose from this model, which is natural considering its re-emphasis of Qwenvergence-14B-v12-Prose-DS. A full evaluation will be cued shortly.

This merge strategy is much simpler than a mainline Lamarck release, but that is necessary to see how multiple fusion merges behave. Where it fits for efforts towards a Lamarck v0.8 depends greatly on evaluation and feedback.

Configuration

The following YAML configuration was used to produce this model:

name:                Lamarck-14B-v0.7-Fusionvergence
merge_method:        arcee_fusion
base_model:          sometimesanotion/Lamarck-14B-v0.7
tokenizer_source:    base
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
dtype:               bfloat16
out_dtype:           bfloat16
models:
  - model:           suayptalha/Lamarckvergence-14B
---
name:                Slerp-Lamarckvevergence
base_model:          sometimesanotion/Lamarck-14B-v0.7-Fusion-Lamarckvergence
merge_method:        slerp
tokenizer_source:    base
dtype:               float32
out_dtype:           bfloat16
parameters:
  t:
    - filter:        self_attn
      value:         [ 0.00, 0.50, 0.30, 0.70, 1.00 ]
    - filter:        mlp
      value:         [ 1.00, 0.50, 0.70, 0.30, 0.00 ]
    - value:         [ 0.00, 0.00, 0.00, 0.00, 0.04, 0.08, 0.12, 0.16, 0.24, 0.32, 0.40, 0.48, 0.56, 0.64, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.64, 0.56, 0.48 ]
slices:
  - sources:
      - model:       sometimesanotion/Lamarck-14B-v0.7-Fusion-Lamarckvergence
        layer_range: [ 0, 48 ]
      - model:       sometimesanotion/Qwenvergence-14B-v12-Prose-DS
        layer_range: [ 0, 48 ]
---
name:                Chocolatine-Fusion-Qwenvergence
merge_method:        arcee_fusion
base_model:          jpacifico/Chocolatine-2-14B-Instruct-v2.0.3
tokenizer_source:    base
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
dtype:               bfloat16
out_dtype:           bfloat16
models:
  - model:           sometimesanotion/Qwenvergence-14B-v12-Prose-DS
---
name:                Lamarck-14B-v0.7-Fusion
merge_method:        arcee_fusion
base_model:          sometimesanotion/Slerp-Lamarckvevergence
tokenizer_source:    base
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
dtype:               bfloat16
out_dtype:           bfloat16
models:
  - model:           sometimesanotion/Chocolatine-Fusion-Qwenvergence

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Safetensors

Model size

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Tensor type

BF16

Model tree for sometimesanotion/Lamarck-14B-v0.7-Fusion

jpacifico/Chocolatine-2-14B-Instruct-v2.0.3

sometimesanotion/Lamarck-14B-v0.7

sometimesanotion/Qwenvergence-14B-v12-Prose-DS

suayptalha/Lamarckvergence-14B

Merge model

this model

Merges

3 models

Quantizations

5 models