karakuri-ai
/

karakuri-lm-7b-apm-v0.2

@@ -1,199 +1,141 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- OpenAssistant/oasst2
+- nvidia/HelpSteer
+language:
+- en
+- ja
+tags:
+- mistral
+- steerlm
+base_model: mistral-community/Mistral-7B-v0.2
 ---
+# KARAKURI LM 7B APM v0.2
 ## Model Details
 ### Model Description
+- **Developed by:** [KARAKURI Inc.](https://about.karakuri.ai/)
+- **Model type:** Causal decoder-only transformer language model
+- **Languages**: Primarily English
+- **License:** Apache 2.0
+- **Finetuned from model:** [mistral-community/Mistral-7B-v0.2](https://huggingface.co/mistral-community/Mistral-7B-v0.2)
+- **Contact**: For questions and comments about the model, please email `karakuri-rd@karakuri.ai`
+## Usage
+KARAKURI LM 7B APM v0.2 is a attribute prediction model that rates model responses on various aspects that makes a response desirable.
+Given a conversation with multiple turns between user and assistant, the model rates the following attributes (between 0 and 4) for every assistant turn.
+- helpfulness: Overall helpfulness of the response to the prompt.
+- correctness: Inclusion of all pertinent facts without errors.
+- coherence: Consistency and clarity of expression.
+- complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
+- verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.
+- quality: Perceived goodness of response.
+- toxicity: Undesirable elements such as vulgar, harmful or potentially biased response.
+- humor: Sense of humor within response.
+- creativity: Willingness to generate non-conventional response.
+The first five are derived from HelpSteer, while the remaining four are derived from OASST2.
+You can run the model using the 🤗 Transformers:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "karakuri-ai/karakuri-lm-7b-apm-v0.2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+messages = [
+    {"role": "user", "content": "Hello!"},
+    {"role": "assistant", "content": "Hello! How can I help you today?"},
+]
+tokenizer.apply_chat_template(
+    messages,
+    label="helpsteer",
+    tokenize=False,
+    add_generation_prompt=True,
+)
+# <bos>[INST] Hello! [/INST] Hello! How can I help you today? [ATTR_1]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    label="helpsteer",
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+outputs = model.generate(input_ids, max_new_tokens=32)
+tokenizer.decode(outputs[0][input_ids.shape[-1]:])
+#  helpfulness: 2 correctness: 1 coherence: 2 complexity: 1 verbosity: 1 [/ATTR_1]<eos>
+messages += [
+    {"role": "label", "content": "helpfulness: 2 correctness: 1 coherence: 2 complexity: 1 verbosity: 1"},
+    {"role": "user", "content": "Thank you!"},
+    {"role": "assistant", "content": "You're welcome! I'm happy to help however I can."},
+]
+tokenizer.apply_chat_template(
+    messages,
+    label="helpsteer",
+    tokenize=False,
+    add_generation_prompt=True,
+)
+# <bos>[INST] Hello! [/INST] Hello! How can I help you today? [ATTR_1] helpfulness: 2 correctness: 1 coherence: 2 complexity: 1 verbosity: 1 [/ATTR_1]<eos>[INST] Thank you! [/INST] You're welcome! I'm happy to help however I can. [ATTR_1]
+messages = [
+    {"role": "user", "content": "Hello!"},
+    {"role": "assistant", "content": "Hello! How can I help you today?"},
+]
+tokenizer.apply_chat_template(
+    messages,
+    label="oasst",
+    tokenize=False,
+    add_generation_prompt=True,
+)
+# <bos>[INST] Hello! [/INST] Hello! How can I help you today? [ATTR_2]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    label="oasst",
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+outputs = model.generate(input_ids, max_new_tokens=32)
+tokenizer.decode(outputs[0][input_ids.shape[-1]:])
+#  quality: 3 toxicity: 1 humor: 1 creativity: 1 [/ATTR_2]<eos>
+```
 ## Training Details
 ### Training Data
+- [OASST2](https://huggingface.co/datasets/OpenAssistant/oasst2)
+- [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer)
+### Training Infrastructure
+- **Hardware**: The model was trained on single node of an Amazon EC2 trn1.32xlarge instance.
+- **Software**: We use code based on [neuronx-nemo-megatron](https://github.com/aws-neuron/neuronx-nemo-megatron).
+## Citation
+```
+@misc{karakuri_lm_7b_apm_v01,
+	author       = { {KARAKURI} {I}nc. },
+	title        = { {KARAKURI} {LM} 7{B} {APM} v0.2 },
+	year         = { 2024 },
+	url          = { https://huggingface.co/karakuri-ai/karakuri-lm-7b-apm-v0.2 },
+	publisher    = { Hugging Face },
+    journal      = { Hugging Face repository }
+}
+```