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Model: squ11z1/claude-oss-350m
Source: Original Platform
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ModelHub XC
2026-07-15 03:42:10 +08:00
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*.safetensors filter=lfs diff=lfs merge=lfs -text
*.gguf filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text

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README.md Normal file
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---
language:
- en
tags:
- text-generation
- claude
- lfm2
- claude-style
- gguf
base_model:
- LiquidAI/LFM2.5-350M
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
license: other
---
# Claude OSS 350M
> **Disclaimer:** This is **not** an official release by Anthropic.
> Claude OSS 350M is an independent open model project.
![claudeoss350](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/3a-HcJ6O-q6EAQHrXzsgI.png)
## Overview
Claude OSS 350M is a compact assistant model built to bring a **familiar Claude-style feel into an edge-sized model**.
In simple terms: this is an attempt to capture the **habitual Claude-style tone and interaction pattern in a lightweight 350M-class model** that is easier to run in constrained environments.
The model was fine-tuned on **open-source datasets**, with a combined total of approximately **200,000 rows** collected from Hugging Face. The training focus emphasized assistant behavior, conversational tone, instruction following, and consistent identity in a small-footprint setting.
Claude OSS 350M is intended for:
- edge deployment
- low-memory experimentation
- lightweight assistant tasks
- fast local inference
- compact multilingual interaction
---

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{{- 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 -%}

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{
"architectures": [
"Lfm2ForCausalLM"
],
"block_auto_adjust_ff_dim": true,
"block_dim": 1024,
"block_ff_dim": 6656,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 1,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 1024,
"conv_use_xavier_init": true,
"dtype": "float32",
"eos_token_id": 7,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 6656,
"layer_types": [
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv"
],
"max_position_embeddings": 128000,
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 16,
"num_heads": 16,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"tie_embedding": true,
"transformers_version": "5.0.0rc1",
"use_cache": true,
"use_pos_enc": true,
"vocab_size": 65536
}

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{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 7,
"pad_token_id": 0,
"transformers_version": "5.0.0rc1"
}

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{
"additional_special_tokens": null,
"backend": "tokenizers",
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"extra_special_tokens": [],
"is_local": false,
"legacy": false,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 1000000000000000019884624838656,
"model_specific_special_tokens": {},
"pad_token": "<|pad|>",
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"tokenizer_class": "TokenizersBackend",
"use_default_system_prompt": false,
"use_fast": true
}

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{
"base_model": "LiquidAI/LFM2.5-350M",
"epochs": 1,
"train_loss": 1.1604940860100041,
"valid_loss": 0.764136791229248,
"test_loss": 0.7036402225494385,
"train_runtime_seconds": 10108.6333,
"valid_runtime_seconds": 44.5711,
"test_runtime_seconds": 37.9638
}