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Model: ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct
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---
library_name: transformers
license: mit
datasets:
- ethicalabs/Kurtis-E1-SFT
language:
- en
base_model:
- Qwen/Qwen2.5-3B-Instruct
pipeline_tag: text-generation
---
# Model Card for Kurtis-E1.1-Qwen2.5-3B-Instruct
Kurtis E1.1 fine-tuned with [flower](https://flower.ai/)
## Eval Results
Evaluation tasks were performed with the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) on an NVIDIA A40.
### hellaswag
```
lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks hellaswag --device cuda:0 --batch_size 8
```
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag| 1|none | 0|acc |↑ |0.5555|± |0.0050|
| | |none | 0|acc_norm|↑ |0.7412|± |0.0044|
### arc_easy
```
lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks arc_easy --device cuda:0 --batch_size 8
```
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|--------|------:|------|-----:|--------|---|-----:|---|-----:|
|arc_easy| 1|none | 0|acc |↑ |0.7710|± |0.0086|
| | |none | 0|acc_norm|↑ |0.6789|± |0.0096|
### arc_challenge
```
lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks arc_challenge --device cuda:0 --batch_size 8
```
| Tasks |Version|Filter|n-shot| Metric | |Value| |Stderr|
|-------------|------:|------|-----:|--------|---|----:|---|-----:|
|arc_challenge| 1|none | 0|acc |↑ |0.436|± |0.0145|
| | |none | 0|acc_norm|↑ |0.448|± |0.0145|
### mmlu
```
lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks mmlu --device cuda:0 --batch_size 8
```
| Tasks |Version|Filter|n-shot|Metric| |Value | |Stderr|
|---------------------------------------|------:|------|-----:|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.6522|± |0.0038|
| - humanities | 2|none | |acc |↑ |0.5734|± |0.0066|
| - formal_logic | 1|none | 0|acc |↑ |0.4603|± |0.0446|
| - high_school_european_history | 1|none | 0|acc |↑ |0.7939|± |0.0316|
| - high_school_us_history | 1|none | 0|acc |↑ |0.8333|± |0.0262|
| - high_school_world_history | 1|none | 0|acc |↑ |0.8397|± |0.0239|
| - international_law | 1|none | 0|acc |↑ |0.7769|± |0.0380|
| - jurisprudence | 1|none | 0|acc |↑ |0.7963|± |0.0389|
| - logical_fallacies | 1|none | 0|acc |↑ |0.7975|± |0.0316|
| - moral_disputes | 1|none | 0|acc |↑ |0.6850|± |0.0250|
| - moral_scenarios | 1|none | 0|acc |↑ |0.2905|± |0.0152|
| - philosophy | 1|none | 0|acc |↑ |0.7106|± |0.0258|
| - prehistory | 1|none | 0|acc |↑ |0.7438|± |0.0243|
| - professional_law | 1|none | 0|acc |↑ |0.4759|± |0.0128|
| - world_religions | 1|none | 0|acc |↑ |0.8246|± |0.0292|
| - other | 2|none | |acc |↑ |0.7087|± |0.0079|
| - business_ethics | 1|none | 0|acc |↑ |0.7300|± |0.0446|
| - clinical_knowledge | 1|none | 0|acc |↑ |0.7321|± |0.0273|
| - college_medicine | 1|none | 0|acc |↑ |0.6705|± |0.0358|
| - global_facts | 1|none | 0|acc |↑ |0.3900|± |0.0490|
| - human_aging | 1|none | 0|acc |↑ |0.7130|± |0.0304|
| - management | 1|none | 0|acc |↑ |0.7961|± |0.0399|
| - marketing | 1|none | 0|acc |↑ |0.8803|± |0.0213|
| - medical_genetics | 1|none | 0|acc |↑ |0.7600|± |0.0429|
| - miscellaneous | 1|none | 0|acc |↑ |0.7957|± |0.0144|
| - nutrition | 1|none | 0|acc |↑ |0.7353|± |0.0253|
| - professional_accounting | 1|none | 0|acc |↑ |0.5426|± |0.0297|
| - professional_medicine | 1|none | 0|acc |↑ |0.6434|± |0.0291|
| - virology | 1|none | 0|acc |↑ |0.4880|± |0.0389|
| - social sciences | 2|none | |acc |↑ |0.7618|± |0.0076|
| - econometrics | 1|none | 0|acc |↑ |0.5439|± |0.0469|
| - high_school_geography | 1|none | 0|acc |↑ |0.7677|± |0.0301|
| - high_school_government_and_politics| 1|none | 0|acc |↑ |0.8860|± |0.0229|
| - high_school_macroeconomics | 1|none | 0|acc |↑ |0.6949|± |0.0233|
| - high_school_microeconomics | 1|none | 0|acc |↑ |0.7773|± |0.0270|
| - high_school_psychology | 1|none | 0|acc |↑ |0.8477|± |0.0154|
| - human_sexuality | 1|none | 0|acc |↑ |0.7786|± |0.0364|
| - professional_psychology | 1|none | 0|acc |↑ |0.7075|± |0.0184|
| - public_relations | 1|none | 0|acc |↑ |0.6818|± |0.0446|
| - security_studies | 1|none | 0|acc |↑ |0.7224|± |0.0287|
| - sociology | 1|none | 0|acc |↑ |0.8458|± |0.0255|
| - us_foreign_policy | 1|none | 0|acc |↑ |0.8400|± |0.0368|
| - stem | 2|none | |acc |↑ |0.6070|± |0.0085|
| - abstract_algebra | 1|none | 0|acc |↑ |0.4700|± |0.0502|
| - anatomy | 1|none | 0|acc |↑ |0.6667|± |0.0407|
| - astronomy | 1|none | 0|acc |↑ |0.6776|± |0.0380|
| - college_biology | 1|none | 0|acc |↑ |0.7222|± |0.0375|
| - college_chemistry | 1|none | 0|acc |↑ |0.5000|± |0.0503|
| - college_computer_science | 1|none | 0|acc |↑ |0.6000|± |0.0492|
| - college_mathematics | 1|none | 0|acc |↑ |0.3400|± |0.0476|
| - college_physics | 1|none | 0|acc |↑ |0.4902|± |0.0497|
| - computer_security | 1|none | 0|acc |↑ |0.7000|± |0.0461|
| - conceptual_physics | 1|none | 0|acc |↑ |0.6468|± |0.0312|
| - electrical_engineering | 1|none | 0|acc |↑ |0.6690|± |0.0392|
| - elementary_mathematics | 1|none | 0|acc |↑ |0.5979|± |0.0253|
| - high_school_biology | 1|none | 0|acc |↑ |0.8129|± |0.0222|
| - high_school_chemistry | 1|none | 0|acc |↑ |0.5813|± |0.0347|
| - high_school_computer_science | 1|none | 0|acc |↑ |0.7800|± |0.0416|
| - high_school_mathematics | 1|none | 0|acc |↑ |0.5037|± |0.0305|
| - high_school_physics | 1|none | 0|acc |↑ |0.4437|± |0.0406|
| - high_school_statistics | 1|none | 0|acc |↑ |0.5972|± |0.0334|
| - machine_learning | 1|none | 0|acc |↑ |0.4554|± |0.0473|
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.6522|± |0.0038|
| - humanities | 2|none | |acc |↑ |0.5734|± |0.0066|
| - other | 2|none | |acc |↑ |0.7087|± |0.0079|
| - social sciences| 2|none | |acc |↑ |0.7618|± |0.0076|
| - stem | 2|none | |acc |↑ |0.6070|± |0.0085|
### mmlu (5-shot)
```
lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks mmlu --device cuda:0 --batch_size 8 --num_fewshot 5
```
| Tasks |Version|Filter|n-shot|Metric| |Value | |Stderr|
|---------------------------------------|------:|------|-----:|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.6629|± |0.0038|
| - humanities | 2|none | |acc |↑ |0.5862|± |0.0067|
| - formal_logic | 1|none | 5|acc |↑ |0.4683|± |0.0446|
| - high_school_european_history | 1|none | 5|acc |↑ |0.7818|± |0.0323|
| - high_school_us_history | 1|none | 5|acc |↑ |0.8284|± |0.0265|
| - high_school_world_history | 1|none | 5|acc |↑ |0.8692|± |0.0219|
| - international_law | 1|none | 5|acc |↑ |0.7769|± |0.0380|
| - jurisprudence | 1|none | 5|acc |↑ |0.7963|± |0.0389|
| - logical_fallacies | 1|none | 5|acc |↑ |0.8098|± |0.0308|
| - moral_disputes | 1|none | 5|acc |↑ |0.7110|± |0.0244|
| - moral_scenarios | 1|none | 5|acc |↑ |0.3464|± |0.0159|
| - philosophy | 1|none | 5|acc |↑ |0.7042|± |0.0259|
| - prehistory | 1|none | 5|acc |↑ |0.7284|± |0.0247|
| - professional_law | 1|none | 5|acc |↑ |0.4759|± |0.0128|
| - world_religions | 1|none | 5|acc |↑ |0.8304|± |0.0288|
| - other | 2|none | |acc |↑ |0.7171|± |0.0078|
| - business_ethics | 1|none | 5|acc |↑ |0.7400|± |0.0441|
| - clinical_knowledge | 1|none | 5|acc |↑ |0.7321|± |0.0273|
| - college_medicine | 1|none | 5|acc |↑ |0.6647|± |0.0360|
| - global_facts | 1|none | 5|acc |↑ |0.4100|± |0.0494|
| - human_aging | 1|none | 5|acc |↑ |0.7220|± |0.0301|
| - management | 1|none | 5|acc |↑ |0.7864|± |0.0406|
| - marketing | 1|none | 5|acc |↑ |0.8889|± |0.0206|
| - medical_genetics | 1|none | 5|acc |↑ |0.7900|± |0.0409|
| - miscellaneous | 1|none | 5|acc |↑ |0.7957|± |0.0144|
| - nutrition | 1|none | 5|acc |↑ |0.7680|± |0.0242|
| - professional_accounting | 1|none | 5|acc |↑ |0.5532|± |0.0297|
| - professional_medicine | 1|none | 5|acc |↑ |0.6471|± |0.0290|
| - virology | 1|none | 5|acc |↑ |0.5120|± |0.0389|
| - social sciences | 2|none | |acc |↑ |0.7735|± |0.0075|
| - econometrics | 1|none | 5|acc |↑ |0.5877|± |0.0463|
| - high_school_geography | 1|none | 5|acc |↑ |0.7828|± |0.0294|
| - high_school_government_and_politics| 1|none | 5|acc |↑ |0.8756|± |0.0238|
| - high_school_macroeconomics | 1|none | 5|acc |↑ |0.7051|± |0.0231|
| - high_school_microeconomics | 1|none | 5|acc |↑ |0.7773|± |0.0270|
| - high_school_psychology | 1|none | 5|acc |↑ |0.8550|± |0.0151|
| - human_sexuality | 1|none | 5|acc |↑ |0.8092|± |0.0345|
| - professional_psychology | 1|none | 5|acc |↑ |0.7288|± |0.0180|
| - public_relations | 1|none | 5|acc |↑ |0.6909|± |0.0443|
| - security_studies | 1|none | 5|acc |↑ |0.7551|± |0.0275|
| - sociology | 1|none | 5|acc |↑ |0.8308|± |0.0265|
| - us_foreign_policy | 1|none | 5|acc |↑ |0.8300|± |0.0378|
| - stem | 2|none | |acc |↑ |0.6159|± |0.0084|
| - abstract_algebra | 1|none | 5|acc |↑ |0.5000|± |0.0503|
| - anatomy | 1|none | 5|acc |↑ |0.6222|± |0.0419|
| - astronomy | 1|none | 5|acc |↑ |0.7500|± |0.0352|
| - college_biology | 1|none | 5|acc |↑ |0.7083|± |0.0380|
| - college_chemistry | 1|none | 5|acc |↑ |0.4700|± |0.0502|
| - college_computer_science | 1|none | 5|acc |↑ |0.6200|± |0.0488|
| - college_mathematics | 1|none | 5|acc |↑ |0.4000|± |0.0492|
| - college_physics | 1|none | 5|acc |↑ |0.4902|± |0.0497|
| - computer_security | 1|none | 5|acc |↑ |0.8200|± |0.0386|
| - conceptual_physics | 1|none | 5|acc |↑ |0.6383|± |0.0314|
| - electrical_engineering | 1|none | 5|acc |↑ |0.6483|± |0.0398|
| - elementary_mathematics | 1|none | 5|acc |↑ |0.5820|± |0.0254|
| - high_school_biology | 1|none | 5|acc |↑ |0.8161|± |0.0220|
| - high_school_chemistry | 1|none | 5|acc |↑ |0.6059|± |0.0344|
| - high_school_computer_science | 1|none | 5|acc |↑ |0.7500|± |0.0435|
| - high_school_mathematics | 1|none | 5|acc |↑ |0.4926|± |0.0305|
| - high_school_physics | 1|none | 5|acc |↑ |0.4702|± |0.0408|
| - high_school_statistics | 1|none | 5|acc |↑ |0.6343|± |0.0328|
| - machine_learning | 1|none | 5|acc |↑ |0.4911|± |0.0475|
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.6629|± |0.0038|
| - humanities | 2|none | |acc |↑ |0.5862|± |0.0067|
| - other | 2|none | |acc |↑ |0.7171|± |0.0078|
| - social sciences| 2|none | |acc |↑ |0.7735|± |0.0075|
| - stem | 2|none | |acc |↑ |0.6159|± |0.0084|

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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a compassionate and empathetic mental-health assistant named Kurtis, trained by ethicalabs.ai.\nYou provide thoughtful and supportive responses to user queries.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a compassionate and empathetic mental-health assistant named Kurtis, trained by ethicalabs.ai.\nYou provide thoughtful and supportive responses to user queries.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

1
vocab.json Normal file

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