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
docs: attach pilot and sealed evaluation reports
Browse files- e4-report.json +206 -0
e4-report.json
ADDED
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| 1 |
+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"report": "e4-sealed",
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| 4 |
+
"generated_at_utc": "2026-09-12",
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| 5 |
+
"suite_id": "e4-humanevalplus-sealed-v1",
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"rows": {
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| 7 |
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"original-bf16": {
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"session": "e4-sealed-20260912T135909Z",
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| 9 |
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"generation_summary": {
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| 10 |
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"task_count": 128,
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| 11 |
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"generation_record_count": 128,
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| 12 |
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"terminal_record_count": 128,
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| 13 |
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"retried_task_count": 0,
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| 14 |
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"infrastructure_failure_count": 0,
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| 15 |
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"duplicate_generation_keys": 0,
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| 16 |
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"total_completion_tokens": 211732,
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| 17 |
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"generated_programs_executed": 0
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| 18 |
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},
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| 19 |
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"extraction_yield": 0.789062,
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| 20 |
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"failed_extraction_count": 27,
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| 21 |
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"scoring_evidence": "reports/e4/e4-sealed-20260912T135909Z/original-bf16/scoring/e3-score-20260912T143512162Z",
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| 22 |
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"scored_task_count": 101,
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| 23 |
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"pass_at_1_base": 0.6138613861386139,
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| 24 |
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"pass_at_1_plus": 0.5544554455445545,
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| 25 |
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"mean_decode_tokens_per_second": 101.3,
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| 26 |
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"pass_count_plus": 56,
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| 27 |
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"pass_count_base": 62,
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| 28 |
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"plus_rate_absence_as_zero": 0.4375,
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| 29 |
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"base_rate_absence_as_zero": 0.4844
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| 30 |
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},
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| 31 |
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"merged-bf16": {
|
| 32 |
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"session": "e4-sealed-20260912T135057Z",
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| 33 |
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"generation_summary": {
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| 34 |
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"task_count": 128,
|
| 35 |
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"generation_record_count": 128,
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| 36 |
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"terminal_record_count": 128,
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| 37 |
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"retried_task_count": 0,
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| 38 |
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"infrastructure_failure_count": 0,
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| 39 |
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"duplicate_generation_keys": 0,
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| 40 |
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"total_completion_tokens": 18992,
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| 41 |
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"generated_programs_executed": 0
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| 42 |
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},
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| 43 |
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"extraction_yield": 0.984375,
|
| 44 |
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"failed_extraction_count": 2,
|
| 45 |
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"scoring_evidence": "reports/e4/e4-sealed-20260912T135057Z/merged-bf16/scoring/e3-score-20260912T135917459Z",
|
| 46 |
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"scored_task_count": 126,
|
| 47 |
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"pass_at_1_base": 0.46825396825396826,
|
| 48 |
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"pass_at_1_plus": 0.40476190476190477,
|
| 49 |
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"mean_decode_tokens_per_second": 113.6,
|
| 50 |
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"pass_count_plus": 51,
|
| 51 |
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"pass_count_base": 59,
|
| 52 |
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"plus_rate_absence_as_zero": 0.3984,
|
| 53 |
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"base_rate_absence_as_zero": 0.4609
|
| 54 |
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},
|
| 55 |
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"q8_0": {
|
| 56 |
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"session": "e4-sealed-20260912T135437Z",
|
| 57 |
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"generation_summary": {
|
| 58 |
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"task_count": 128,
|
| 59 |
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"generation_record_count": 128,
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| 60 |
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"terminal_record_count": 128,
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| 61 |
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"retried_task_count": 0,
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| 62 |
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"infrastructure_failure_count": 0,
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"duplicate_generation_keys": 0,
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"total_completion_tokens": 16425,
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| 65 |
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"generated_programs_executed": 0
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| 66 |
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},
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| 67 |
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"extraction_yield": 0.992188,
|
| 68 |
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"failed_extraction_count": 1,
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| 69 |
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"scoring_evidence": "reports/e4/e4-sealed-20260912T135437Z/q8_0/scoring/e3-score-20260912T140124851Z",
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| 70 |
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"scored_task_count": 127,
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| 71 |
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"pass_at_1_base": 0.4645669291338583,
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| 72 |
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"pass_at_1_plus": 0.41732283464566927,
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| 73 |
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"mean_decode_tokens_per_second": 181.8,
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| 74 |
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"pass_count_plus": 53,
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| 75 |
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"pass_count_base": 59,
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| 76 |
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"plus_rate_absence_as_zero": 0.4141,
|
| 77 |
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"base_rate_absence_as_zero": 0.4609
|
| 78 |
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},
|
| 79 |
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"q6_k": {
|
| 80 |
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"session": "e4-sealed-20260912T135648Z",
|
| 81 |
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"generation_summary": {
|
| 82 |
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"task_count": 128,
|
| 83 |
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"generation_record_count": 128,
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| 84 |
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"terminal_record_count": 128,
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| 85 |
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"retried_task_count": 0,
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| 86 |
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"infrastructure_failure_count": 0,
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| 87 |
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"duplicate_generation_keys": 0,
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| 88 |
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"total_completion_tokens": 19346,
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| 89 |
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"generated_programs_executed": 0
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| 90 |
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},
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| 91 |
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"extraction_yield": 0.984375,
|
| 92 |
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"failed_extraction_count": 2,
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| 93 |
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"scoring_evidence": "reports/e4/e4-sealed-20260912T135648Z/q6_k/scoring/e3-score-20260912T140241797Z",
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| 94 |
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"scored_task_count": 126,
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| 95 |
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"pass_at_1_base": 0.42857142857142855,
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| 96 |
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"pass_at_1_plus": 0.3888888888888889,
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| 97 |
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"mean_decode_tokens_per_second": 188.5,
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| 98 |
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"pass_count_plus": 49,
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| 99 |
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"pass_count_base": 54,
|
| 100 |
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"plus_rate_absence_as_zero": 0.3828,
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| 101 |
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"base_rate_absence_as_zero": 0.4219
|
| 102 |
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}
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| 103 |
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},
|
| 104 |
+
"paired_deltas": {
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| 105 |
+
"note": "Paired comparisons use only tasks scored in both rows (extraction failures score zero by absence).",
|
| 106 |
+
"original_vs_merged": {
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| 107 |
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"common_scored_tasks": 99,
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| 108 |
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"merged_plus_pass_count": 49,
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| 109 |
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"original_plus_pass_count": 56,
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| 110 |
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"fine_tune_gained": [
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| 111 |
+
"HumanEval/104",
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| 112 |
+
"HumanEval/105",
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| 113 |
+
"HumanEval/12",
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| 114 |
+
"HumanEval/13",
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| 115 |
+
"HumanEval/149",
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| 116 |
+
"HumanEval/21",
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| 117 |
+
"HumanEval/23",
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| 118 |
+
"HumanEval/25",
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| 119 |
+
"HumanEval/28",
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| 120 |
+
"HumanEval/35",
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| 121 |
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"HumanEval/5",
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| 122 |
+
"HumanEval/53",
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| 123 |
+
"HumanEval/59",
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| 124 |
+
"HumanEval/60",
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| 125 |
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"HumanEval/61",
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| 126 |
+
"HumanEval/8"
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| 127 |
+
],
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| 128 |
+
"fine_tune_lost": [
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| 129 |
+
"HumanEval/100",
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| 130 |
+
"HumanEval/103",
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| 131 |
+
"HumanEval/110",
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| 132 |
+
"HumanEval/111",
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| 133 |
+
"HumanEval/121",
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| 134 |
+
"HumanEval/127",
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| 135 |
+
"HumanEval/139",
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| 136 |
+
"HumanEval/141",
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| 137 |
+
"HumanEval/151",
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| 138 |
+
"HumanEval/152",
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| 139 |
+
"HumanEval/156",
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| 140 |
+
"HumanEval/159",
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| 141 |
+
"HumanEval/161",
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| 142 |
+
"HumanEval/24",
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| 143 |
+
"HumanEval/36",
|
| 144 |
+
"HumanEval/41",
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| 145 |
+
"HumanEval/49",
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| 146 |
+
"HumanEval/69",
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| 147 |
+
"HumanEval/70",
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| 148 |
+
"HumanEval/71",
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| 149 |
+
"HumanEval/81",
|
| 150 |
+
"HumanEval/95",
|
| 151 |
+
"HumanEval/98"
|
| 152 |
+
]
|
| 153 |
+
},
|
| 154 |
+
"merged_vs_q8_0": {
|
| 155 |
+
"common_scored_tasks": 126,
|
| 156 |
+
"quant_lost_vs_merged": [
|
| 157 |
+
"HumanEval/105"
|
| 158 |
+
],
|
| 159 |
+
"quant_gained_vs_merged": [
|
| 160 |
+
"HumanEval/88",
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| 161 |
+
"HumanEval/89"
|
| 162 |
+
]
|
| 163 |
+
},
|
| 164 |
+
"merged_vs_q6_k": {
|
| 165 |
+
"common_scored_tasks": 126,
|
| 166 |
+
"quant_lost_vs_merged": [
|
| 167 |
+
"HumanEval/105",
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| 168 |
+
"HumanEval/146",
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| 169 |
+
"HumanEval/80"
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| 170 |
+
],
|
| 171 |
+
"quant_gained_vs_merged": [
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| 172 |
+
"HumanEval/70"
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| 173 |
+
]
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| 174 |
+
}
|
| 175 |
+
},
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| 176 |
+
"deployment_gate_verdict": {
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| 177 |
+
"selected_quant": "q6_k",
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| 178 |
+
"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",
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| 179 |
+
"delta_vs_merged_pp": -1.59,
|
| 180 |
+
"paired_losses": [
|
| 181 |
+
"HumanEval/105",
|
| 182 |
+
"HumanEval/146",
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| 183 |
+
"HumanEval/80"
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| 184 |
+
],
|
| 185 |
+
"paired_gains": [
|
| 186 |
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"HumanEval/70"
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| 187 |
+
],
|
| 188 |
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"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.",
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| 189 |
+
"runner_up": {
|
| 190 |
+
"alias": "q8_0",
|
| 191 |
+
"note": "net +1 paired task vs merged-bf16 at 1246 MB"
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| 192 |
+
}
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| 193 |
+
},
|
| 194 |
+
"findings": {
|
| 195 |
+
"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.",
|
| 196 |
+
"quantization_effect": "Q8_0 and Q6_K stay within noise of merged BF16 on paired tasks.",
|
| 197 |
+
"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.",
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| 198 |
+
"harness_health": "Zero infrastructure failures, zero retries, zero duplicate keys across all four rows."
|
| 199 |
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},
|
| 200 |
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"caveats": [
|
| 201 |
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"Scorer pass@1 excludes unextractable outputs from the denominator; plus_rate_absence_as_zero is the deployment-honest rate and is reported alongside.",
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| 202 |
+
"One greedy sample per task; no seed variance measured (deterministic regression track).",
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| 203 |
+
"KodCode contamination caveat applies to absolute scores; paired deltas remain the scientific comparison.",
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| 204 |
+
"HumanEval/105 is a shared quantization-sensitive task lost by both quants."
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| 205 |
+
]
|
| 206 |
+
}
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