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
Download e4-report.json from enseven/lfm-2.5-think-code: direct link, hf CLI and curl.
- Browser
- Download file 7.75 kB
-
https://huggingface.co/enseven/lfm-2.5-think-code/resolve/main/e4-report.json
- Command line
-
hf download hf://enseven/lfm-2.5-think-code/e4-report.json
-
curl -L -o e4-report.json https://huggingface.co/enseven/lfm-2.5-think-code/resolve/main/e4-report.json
7.75 kB
| { | |
| "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." | |
| ] | |
| } | |