Text Generation
PEFT
Safetensors
English
lora
unsloth
lfm2
code
pull-request
model-inversion
conversational
Instructions to use Codingstark/LFM2.5-350M-PR-Origin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Codingstark/LFM2.5-350M-PR-Origin with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-350M") model = PeftModel.from_pretrained(base_model, "Codingstark/LFM2.5-350M-PR-Origin") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Codingstark/LFM2.5-350M-PR-Origin with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Codingstark/LFM2.5-350M-PR-Origin to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Codingstark/LFM2.5-350M-PR-Origin to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Codingstark/LFM2.5-350M-PR-Origin to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Codingstark/LFM2.5-350M-PR-Origin", max_seq_length=2048, )
File size: 2,553 Bytes
e82ccf1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | {{- bos_token -}}
{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
{%- set ns = namespace(system_prompt="") -%}
{%- if messages[0]["role"] == "system" -%}
{%- set sys_content = messages[0]["content"] -%}
{%- if sys_content is not string -%}
{%- for item in sys_content -%}
{%- if item["type"] == "text" -%}
{%- set ns.system_prompt = ns.system_prompt + item["text"] -%}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{%- set ns.system_prompt = sys_content -%}
{%- endif -%}
{%- set messages = messages[1:] -%}
{%- endif -%}
{%- if tools -%}
{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
{%- for tool in tools -%}
{%- if tool is not string -%}
{%- set tool = tool | tojson -%}
{%- endif -%}
{%- set ns.system_prompt = ns.system_prompt + tool -%}
{%- if not loop.last -%}
{%- set ns.system_prompt = ns.system_prompt + ", " -%}
{%- endif -%}
{%- endfor -%}
{%- set ns.system_prompt = ns.system_prompt + "]" -%}
{%- endif -%}
{%- if ns.system_prompt -%}
{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
{%- endif -%}
{%- set ns.last_assistant_index = -1 -%}
{%- for message in messages -%}
{%- if message["role"] == "assistant" -%}
{%- set ns.last_assistant_index = loop.index0 -%}
{%- endif -%}
{%- endfor -%}
{%- for message in messages -%}
{{- "<|im_start|>" + message["role"] + "\n" -}}
{%- set content = message["content"] -%}
{%- if content is not string -%}
{%- set ns.content = "" -%}
{%- for item in content -%}
{%- if item["type"] == "image" -%}
{%- set ns.content = ns.content + "<image>" -%}
{%- elif item["type"] == "text" -%}
{%- set ns.content = ns.content + item["text"] -%}
{%- else -%}
{%- set ns.content = ns.content + item | tojson -%}
{%- endif -%}
{%- endfor -%}
{%- set content = ns.content -%}
{%- endif -%}
{%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
{%- if "</think>" in content -%}
{%- set content = content.split("</think>")[-1] | trim -%}
{%- endif -%}
{%- endif -%}
{{- content + "<|im_end|>\n" -}}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- "<|im_start|>assistant\n" -}}
{%- endif -%} |