Instructions to use Noodlz/DolphinStar-12.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noodlz/DolphinStar-12.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Noodlz/DolphinStar-12.5B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Noodlz/DolphinStar-12.5B") model = AutoModelForCausalLM.from_pretrained("Noodlz/DolphinStar-12.5B") 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
- vLLM
How to use Noodlz/DolphinStar-12.5B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Noodlz/DolphinStar-12.5B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Noodlz/DolphinStar-12.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Noodlz/DolphinStar-12.5B
- SGLang
How to use Noodlz/DolphinStar-12.5B 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 "Noodlz/DolphinStar-12.5B" \ --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": "Noodlz/DolphinStar-12.5B", "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 "Noodlz/DolphinStar-12.5B" \ --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": "Noodlz/DolphinStar-12.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Noodlz/DolphinStar-12.5B with Docker Model Runner:
docker model run hf.co/Noodlz/DolphinStar-12.5B
Custom Model "Dolphin2Star1" Merged by Noodlz. 12.5B linear merged from the uncensored mistral 7B v0.2 as the base, with the fine tunes of StarlingLM 7B Beta that's originally mistral 7B v0.1
have fun =)
[EDIT] - preset wise it seems like it likes the "ChatML" format. [EDIT 2] - Usage Notes - model is sorta picky with the batch size and prompt preset/template. (maybe because merge of ChatML and OpenChat models)
My current recommended setting & findings
- Using LM Studio - use the default preset. GPU acceleration to max. prompt eval size to 1024, context length to 32768. this yields me decent, coherant results. ChatML works too but occasionall spits up odd texts after a couple of turns.
- Using Oobabooga (Windows PC) - runs well using run-in-4bit along with use_flash_attention_2. default presets and everything works just fine.
- Using OobaBooga (Mac) - [investigating]
Instructions Template:
{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<s>' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}
Chat Template:
{%- for message in messages %}
{%- if message['role'] == 'system' -%}
{%- if message['content'] -%}
{{- message['content'] + '\n\n' -}}
{%- endif -%}
{%- if user_bio -%}
{{- user_bio + '\n\n' -}}
{%- endif -%}
{%- else -%}
{%- if message['role'] == 'user' -%}
{{- name1 + ': ' + message['content'] + '\n'-}}
{%- else -%}
{{- name2 + ': ' + message['content'] + '\n' -}}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
license: apache-2.0
base_model:
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
- NexusFlow/Starling-LM-7B-beta library_name: transformers tags:
- mergekit
- merge
output_folder
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
merge_method: linear
parameters:
weight: 1.0
slices:
- sources:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [0,1]
- model: NexusFlow/Starling-LM-7B-beta
layer_range: [0,1]
parameters:
weight: 0
- sources:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [1,8]
- sources:
- model: NexusFlow/Starling-LM-7B-beta
layer_range: [4,12]
- sources:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [8,16]
- sources:
- model: NexusFlow/Starling-LM-7B-beta
layer_range: [12,20]
- sources:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [16,24]
- sources:
- model: NexusFlow/Starling-LM-7B-beta
layer_range: [20,28]
- sources:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [24,31]
- sources:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [31,32]
- model: NexusFlow/Starling-LM-7B-beta
layer_range: [31,32]
parameters:
weight: 0
dtype: float16
tokenizer_source: model:cognitivecomputations/dolphin-2.8-mistral-7b-v02
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