Instructions to use enseven/lfm-2.5-think-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use enseven/lfm-2.5-think-code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-Thinking") model = PeftModel.from_pretrained(base_model, "enseven/lfm-2.5-think-code") - Notebooks
- Google Colab
- Kaggle
File size: 7,751 Bytes
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"schema_version": 1,
"report": "e4-sealed",
"generated_at_utc": "2026-09-12",
"suite_id": "e4-humanevalplus-sealed-v1",
"rows": {
"original-bf16": {
"session": "e4-sealed-20260912T135909Z",
"generation_summary": {
"task_count": 128,
"generation_record_count": 128,
"terminal_record_count": 128,
"retried_task_count": 0,
"infrastructure_failure_count": 0,
"duplicate_generation_keys": 0,
"total_completion_tokens": 211732,
"generated_programs_executed": 0
},
"extraction_yield": 0.789062,
"failed_extraction_count": 27,
"scoring_evidence": "reports/e4/e4-sealed-20260912T135909Z/original-bf16/scoring/e3-score-20260912T143512162Z",
"scored_task_count": 101,
"pass_at_1_base": 0.6138613861386139,
"pass_at_1_plus": 0.5544554455445545,
"mean_decode_tokens_per_second": 101.3,
"pass_count_plus": 56,
"pass_count_base": 62,
"plus_rate_absence_as_zero": 0.4375,
"base_rate_absence_as_zero": 0.4844
},
"merged-bf16": {
"session": "e4-sealed-20260912T135057Z",
"generation_summary": {
"task_count": 128,
"generation_record_count": 128,
"terminal_record_count": 128,
"retried_task_count": 0,
"infrastructure_failure_count": 0,
"duplicate_generation_keys": 0,
"total_completion_tokens": 18992,
"generated_programs_executed": 0
},
"extraction_yield": 0.984375,
"failed_extraction_count": 2,
"scoring_evidence": "reports/e4/e4-sealed-20260912T135057Z/merged-bf16/scoring/e3-score-20260912T135917459Z",
"scored_task_count": 126,
"pass_at_1_base": 0.46825396825396826,
"pass_at_1_plus": 0.40476190476190477,
"mean_decode_tokens_per_second": 113.6,
"pass_count_plus": 51,
"pass_count_base": 59,
"plus_rate_absence_as_zero": 0.3984,
"base_rate_absence_as_zero": 0.4609
},
"q8_0": {
"session": "e4-sealed-20260912T135437Z",
"generation_summary": {
"task_count": 128,
"generation_record_count": 128,
"terminal_record_count": 128,
"retried_task_count": 0,
"infrastructure_failure_count": 0,
"duplicate_generation_keys": 0,
"total_completion_tokens": 16425,
"generated_programs_executed": 0
},
"extraction_yield": 0.992188,
"failed_extraction_count": 1,
"scoring_evidence": "reports/e4/e4-sealed-20260912T135437Z/q8_0/scoring/e3-score-20260912T140124851Z",
"scored_task_count": 127,
"pass_at_1_base": 0.4645669291338583,
"pass_at_1_plus": 0.41732283464566927,
"mean_decode_tokens_per_second": 181.8,
"pass_count_plus": 53,
"pass_count_base": 59,
"plus_rate_absence_as_zero": 0.4141,
"base_rate_absence_as_zero": 0.4609
},
"q6_k": {
"session": "e4-sealed-20260912T135648Z",
"generation_summary": {
"task_count": 128,
"generation_record_count": 128,
"terminal_record_count": 128,
"retried_task_count": 0,
"infrastructure_failure_count": 0,
"duplicate_generation_keys": 0,
"total_completion_tokens": 19346,
"generated_programs_executed": 0
},
"extraction_yield": 0.984375,
"failed_extraction_count": 2,
"scoring_evidence": "reports/e4/e4-sealed-20260912T135648Z/q6_k/scoring/e3-score-20260912T140241797Z",
"scored_task_count": 126,
"pass_at_1_base": 0.42857142857142855,
"pass_at_1_plus": 0.3888888888888889,
"mean_decode_tokens_per_second": 188.5,
"pass_count_plus": 49,
"pass_count_base": 54,
"plus_rate_absence_as_zero": 0.3828,
"base_rate_absence_as_zero": 0.4219
}
},
"paired_deltas": {
"note": "Paired comparisons use only tasks scored in both rows (extraction failures score zero by absence).",
"original_vs_merged": {
"common_scored_tasks": 99,
"merged_plus_pass_count": 49,
"original_plus_pass_count": 56,
"fine_tune_gained": [
"HumanEval/104",
"HumanEval/105",
"HumanEval/12",
"HumanEval/13",
"HumanEval/149",
"HumanEval/21",
"HumanEval/23",
"HumanEval/25",
"HumanEval/28",
"HumanEval/35",
"HumanEval/5",
"HumanEval/53",
"HumanEval/59",
"HumanEval/60",
"HumanEval/61",
"HumanEval/8"
],
"fine_tune_lost": [
"HumanEval/100",
"HumanEval/103",
"HumanEval/110",
"HumanEval/111",
"HumanEval/121",
"HumanEval/127",
"HumanEval/139",
"HumanEval/141",
"HumanEval/151",
"HumanEval/152",
"HumanEval/156",
"HumanEval/159",
"HumanEval/161",
"HumanEval/24",
"HumanEval/36",
"HumanEval/41",
"HumanEval/49",
"HumanEval/69",
"HumanEval/70",
"HumanEval/71",
"HumanEval/81",
"HumanEval/95",
"HumanEval/98"
]
},
"merged_vs_q8_0": {
"common_scored_tasks": 126,
"quant_lost_vs_merged": [
"HumanEval/105"
],
"quant_gained_vs_merged": [
"HumanEval/88",
"HumanEval/89"
]
},
"merged_vs_q6_k": {
"common_scored_tasks": 126,
"quant_lost_vs_merged": [
"HumanEval/105",
"HumanEval/146",
"HumanEval/80"
],
"quant_gained_vs_merged": [
"HumanEval/70"
]
}
},
"deployment_gate_verdict": {
"selected_quant": "q6_k",
"gate_frozen": "Q6_K deployable iff sealed pass@1(plus) within 2 pp of merged-bf16 AND no new paired failure class absent from merged-bf16",
"delta_vs_merged_pp": -1.59,
"paired_losses": [
"HumanEval/105",
"HumanEval/146",
"HumanEval/80"
],
"paired_gains": [
"HumanEval/70"
],
"assessment": "PASS: -1.59 pp (within the 2 pp gate); three scattered single-task losses with one gain is quantization noise near token ties, not a new failure class. Q8_0 is quality-equivalent-or-better (net +1 task) but 296 MB larger; Q6_K remains the frozen selection.",
"runner_up": {
"alias": "q8_0",
"note": "net +1 paired task vs merged-bf16 at 1246 MB"
}
},
"findings": {
"fine_tune_effect": "Negative, but smaller than the pilot suggested: on the 99 tasks scored in both rows the fine-tune passes 49 versus the original 56 (16 gained, 23 lost, net -7, -7.1 pp paired). Counting every unextractable output as a deployed failure: original 0.438 vs merged 0.398 of 128.",
"quantization_effect": "Q8_0 and Q6_K stay within noise of merged BF16 on paired tasks.",
"original_reasoning_leakage": "The original model emitted 211,732 completion tokens (about 1,654 per task) with 27 outputs unextractable; the fine-tune emitted 18,992 tokens (about 148 per task) with 2 unextractable.",
"harness_health": "Zero infrastructure failures, zero retries, zero duplicate keys across all four rows."
},
"caveats": [
"Scorer pass@1 excludes unextractable outputs from the denominator; plus_rate_absence_as_zero is the deployment-honest rate and is reported alongside.",
"One greedy sample per task; no seed variance measured (deterministic regression track).",
"KodCode contamination caveat applies to absolute scores; paired deltas remain the scientific comparison.",
"HumanEval/105 is a shared quantization-sensitive task lost by both quants."
]
}
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