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Model: ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF Source: Original Platform
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README.md
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---
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base_model: ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth
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tags:
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- gguf
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- llama.cpp
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- unsloth
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- lfm2
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- reasoning
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- quantized
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license: apache-2.0
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language:
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- en
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datasets:
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- ermiaazarkhalili/Claude-Opus-4.7-Reasoning
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pipeline_tag: text-generation
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---
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# LFM2.5-1.2B-SFT-Unsloth — GGUF quantized
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GGUF quantizations of [`ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth),
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produced via [Unsloth](https://github.com/unslothai/unsloth) + llama.cpp's conversion scripts.
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| Field | Value |
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|---|---|
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| **Source checkpoint** | [`ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth) |
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| **Base model** | [`LiquidAI/LFM2.5-1.2B-Instruct`](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) |
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| **Dataset** | [`ermiaazarkhalili/Claude-Opus-4.7-Reasoning`](https://huggingface.co/datasets/ermiaazarkhalili/Claude-Opus-4.7-Reasoning) |
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| **Training** | N=1 full epoch (N=1 epoch steps, effective batch=8) |
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| **Conversion** | Unsloth `save_pretrained_gguf` → llama.cpp GGUF |
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| **Quantization tool** | llama.cpp `llama-quantize` |
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## Available quantizations
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| File | Size | Notes |
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|---|---|---|
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| `LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth.Q2_K.gguf` | smallest | 2-bit; extreme compression, quality loss |
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| `LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth.Q3_K_M.gguf` | small | 3-bit; modest quality trade-off |
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| `LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth.Q4_K_M.gguf` | recommended | 4-bit; best size/quality balance |
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| `LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth.Q5_K_M.gguf` | balanced | 5-bit; near-full quality |
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| `LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth.Q6_K.gguf` | high quality | 6-bit; minimal degradation |
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| `LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth.Q8_0.gguf` | largest | 8-bit; closest to bf16 source |
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**Recommended default:** `Q4_K_M` (4-bit, K-quant medium). For memory-constrained deployment, try `Q2_K` or `Q3_K_M`. For maximum fidelity, use `Q8_0`.
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## Usage
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### llama.cpp
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```bash
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# Text-only
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llama-cli -hf ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF --jinja -p "Explain step-by-step: if a train travels 60 mph for 2.5 hours, how far does it go?" -n 256
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# Interactive chat
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llama-cli -hf ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF --jinja -cnv
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```
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### Ollama
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```bash
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ollama run hf.co/ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF:Q4_K_M
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```
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### llama-cpp-python
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF",
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filename="*Q4_K_M.gguf",
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n_ctx=2048,
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)
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out = llm.create_chat_completion(
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messages=[{"role": "user", "content": "Explain step-by-step: if a train travels 60 mph for 2.5 hours, how far does it go?"}],
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max_tokens=256,
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)
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print(out["choices"][0]["message"]["content"])
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```
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## Intended use
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For research and non-commercial experimentation only. Outputs should be independently verified before any downstream use.
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## Limitations
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- GGUF quantizations have unavoidable quality loss relative to the source bfloat16 checkpoint. Use `Q5_K_M` or `Q8_0` for best fidelity.
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- Inherits all limitations of the source merged checkpoint ([`ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth)).
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- Distilled reasoning traces reflect patterns from Claude Opus 4.7 and may not generalize to domains outside the distillation corpus.
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## Citation
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```bibtex
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@misc{ lfm25_12b_sft_claude_opus_2026_gguf ,
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author = {Ermia Azarkhalili},
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title = { LFM2.5-1.2B-SFT-Unsloth — GGUF quantized },
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF}}
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}
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```
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---
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This lfm2 model was trained 2× faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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config.json
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{
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"architectures": [
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"Lfm2ForCausalLM"
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],
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"block_auto_adjust_ff_dim": true,
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"block_dim": 2048,
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"block_ffn_dim_multiplier": 1.0,
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"block_mlp_init_scale": 1.0,
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"block_multiple_of": 256,
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"block_norm_eps": 1e-05,
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"block_out_init_scale": 1.0,
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"block_use_swiglu": true,
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"block_use_xavier_init": true,
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"bos_token_id": 1,
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"conv_L_cache": 3,
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"conv_bias": false,
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"conv_dim": 2048,
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"conv_use_xavier_init": true,
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"torch_dtype": "bfloat16",
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"eos_token_id": 7,
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"full_attn_idxs": null,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv"
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],
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"max_position_embeddings": 128000,
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"model_name": "LiquidAI/LFM2.5-1.2B-Instruct",
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"model_type": "lfm2",
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"norm_eps": 1e-05,
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"num_attention_heads": 32,
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"num_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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"unsloth_version": "2026.4.6",
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"use_cache": false,
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"use_pos_enc": true,
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"vocab_size": 65536
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}
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