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README.md

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+---
+license: other
+license_name: lfm1.0
+license_link: https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE
+base_model: LiquidAI/LFM2.5-230M
+tags:
+  - lfm2
+  - lfm2.5
+  - liquid
+  - code
+  - math
+  - fine-tune
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# LFM2.5-230M-Code-Math
+
+A fine-tune of [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) (the instruct-tuned model, **not** the base checkpoint) focused on strengthening code generation and math word-problem solving, while retaining the general chat and instruction-following ability of the original instruct model.
+
+## Why this exists
+
+LiquidAI's own model card for LFM2.5-230M states it is **not recommended for reasoning-heavy workloads such as advanced math, code generation, or creative writing** — the model is tuned primarily for data extraction, structured outputs, and lightweight agentic/tool-use tasks. This fine-tune is an attempt to push a small, efficient instruct model further into code and math competence without sacrificing its existing conversational ability.
+
+Fine-tuning started from the **instruct** checkpoint rather than the base pretrain checkpoint, specifically to preserve chat and instruction-following behavior that the base model doesn't have. An earlier fine-tune attempt starting from `LFM2.5-230M-Base` produced a model that was strong at code/math but broke down on basic conversation (e.g. echoing "Hello, who are you?" back verbatim). Starting from instruct avoided this.
+
+## Training details
+
+- **Base model**: `LiquidAI/LFM2.5-230M` (instruct)
+- **Method**: Full fine-tune (LoRA would also work at this scale; full-FT was used here since compute wasn't a constraint)
+- **Datasets**:
+  - Code: [`iamtarun/code_instructions_120k_alpaca`](https://huggingface.co/datasets/iamtarun/code_instructions_120k_alpaca)
+  - Math: [`openai/gsm8k`](https://huggingface.co/datasets/openai/gsm8k) (main split)
+- **Checkpoint selection**: best checkpoint by eval loss (not final step) — training showed clear overfitting past ~step 7500, where training loss kept falling but eval loss plateaued/rose slightly. The published checkpoint is from before that point.
+- **Sequence length**: 1024 tokens (dataset is short-form; base model supports up to 32K context)
+- **Loss**: completion-only (loss computed only on assistant responses, not prompts)
+
+## What it's good at
+
+Based on manual testing across ~20+ prompts spanning algebra, geometry, general code tasks, and open-ended chat:
+
+- **Code**: Reliable on common patterns — string/list manipulation, simple classes, recursion, file I/O, prime checking, etc. In the author's own informal side-by-side testing, output was clearer and more consistent than Qwen2.5-Coder-0.5B-Instruct on the same prompts. This is a subjective, single-user comparison, not a formal benchmark — your results may differ.
+- **Math**: Grade-school word problems (gsm8k-style), percentages, basic algebra, geometry (area/perimeter) — mostly correct with gsm8k-style step annotations.
+- **Chat**: Retains coherent, on-topic conversational ability inherited from the instruct base — no repetition loops or echo failures observed in testing.
+- **Tool calling**: Spot-checked informally by the author using the Pythonic tool-call format LFM2.5 supports; not systematically benchmarked against other models.
+
+## Known limitations
+
+- Occasional arithmetic slip on multi-step algebra (e.g., correct method shown, final division not simplified).
+- Not tested on data extraction or RAG.
+- Still a 230M-parameter model — do not expect deep multi-step reasoning, advanced math, or long-form creative writing at the level of much larger models.
+- Not evaluated on safety-critical, medical, or legal use cases — do not use for those without additional safeguards.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "hauser458original/lfm2.5-230m-code-math"
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [{"role": "user", "content": "Write a Python function to check if a number is prime."}]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.3, top_p=0.9)
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+GGUF quantized versions (Q4_K_M, Q5_K_S, Q5_K_M, Q8_0, F16) for llama.cpp/Ollama/LM Studio are available at: `hauser458original/lfm2.5-230m-code-math-GGUF`
+
+## License
+
+Inherits the [LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE) from the base model.
+
+## Acknowledgements
+
+Built on [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M). See the [LFM2 Technical Report](https://arxiv.org/abs/2511.23404) for details on the base architecture.

chat_template.jinja

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+{{- bos_token -}}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+
+{%- macro format_arg_value(arg_value) -%}
+    {%- if arg_value is string -%}
+        {{- "'" + arg_value + "'" -}}
+    {%- elif arg_value is mapping -%}
+        {{- arg_value | tojson -}}
+    {%- else -%}
+        {{- arg_value | string -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro parse_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- else -%}
+        {%- set _ns = namespace(result="") -%}
+        {%- for item in content -%}
+            {%- if item["type"] == "image" -%}
+                {%- set _ns.result = _ns.result + "<image>" -%}
+            {%- elif item["type"] == "text" -%}
+                {%- set _ns.result = _ns.result + item["text"] -%}
+            {%- else -%}
+                {%- set _ns.result = _ns.result + item | tojson -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {{- _ns.result -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_calls(tool_calls) -%}
+    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}
+    {%- for tool_call in tool_calls -%}
+        {%- set func_name = tool_call["function"]["name"] -%}
+        {%- set func_args = tool_call["function"]["arguments"] -%}
+        {%- set args_ns = namespace(arg_strings=[]) -%}
+        {%- for arg_name, arg_value in func_args.items() -%}
+            {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
+        {%- endfor -%}
+        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
+    {%- endfor -%}
+    {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(system_prompt="", last_user_index=-1) -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- if messages[0].get("content") -%}
+        {%- set ns.system_prompt = parse_content(messages[0]["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 -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message.role + "\n" -}}
+    {%- if message.role == "assistant" -%}
+        {%- generation -%}
+        {%- if message.thinking is defined and (preserve_thinking or loop.index0 > ns.last_user_index) -%}
+            {{- "<think>" + message.thinking + "</think>" -}}
+        {%- endif -%}
+        {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%}
+        {%- set _has_cfm = false -%}
+        {%- if message.content is defined -%}
+            {%- set content = parse_content(message.content) -%}
+            {%- if not (preserve_thinking or loop.index0 > ns.last_user_index) -%}
+                {%- if "</think>" in content -%}
+                    {%- set content = content.split("</think>")[-1] | trim -%}
+                {%- endif -%}
+            {%- endif -%}
+            {%- if message.tool_calls is defined and content.endswith(_cfm_tag) -%}
+                {%- set _has_cfm = true -%}
+                {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}
+                {{- content[:_trunc_len] -}}
+            {%- else -%}
+                {{- content -}}
+            {%- endif -%}
+        {%- endif -%}
+        {%- if message.tool_calls is defined -%}
+            {{- render_tool_calls(message.tool_calls) -}}
+        {%- endif -%}
+        {%- if _has_cfm -%}
+            {{- _cfm_tag -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+        {%- endgeneration -%}
+    {%- else %}
+        {%- if message.get("content") -%}
+            {{- parse_content(message["content"]) -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+    {%- endif %}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block__name_mlp": "parallel_mlp_merged",
+  "block_auto_adjust_ff_dim": false,
+  "block_dim": 1024,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_ffn_te_autocast": false,
+  "block_ffn_use_quantized_params": false,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_sequence_parallel_norm_across_tp": false,
+  "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": "bfloat16",
+  "eos_token_id": 7,
+  "ffn_te_autocast": false,
+  "ffn_use_quantized_params": false,
+  "full_attn_idxs": null,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "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": 14,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sequence_parallel_norm_across_tp": false,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": false,
+  "use_pos_enc": true,
+  "vocab_size": 65536
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": [
+    7
+  ],
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 50,
+  "transformers_version": "5.13.1",
+  "use_cache": true
+}

model.safetensors

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+size 459401112

optimizer.pt

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rng_state.pth

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scheduler.pt

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+size 1465

tokenizer_config.json

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

training_args.bin

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+size 5713