Update README.md

README.md

@@ -1,202 +1,79 @@
 ---
 library_name: peft
 base_model: mistralai/Mistral-7B-v0.1
+tags:
+  - axolotl
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+A model that can generate [Honeycomb Queries](https://www.honeycomb.io/blog/introducing-query-assistant).  
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+> fine-tuned by [Hamel Husain] 
 
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## How to Get Started with the Model
 
-[More Information Needed]
+Make sure you install all dependencies
 
-### Out-of-Scope Use
+```bash
+pip install transformers datasets peft accelerate bitsandbytes safetensors --upgrade
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Next, load the dependencies.
 
-[More Information Needed]
+```python
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+model_id='hamel/hc-mistral-qlora-6'
+model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token = tokenizer.eos_token
+```
 
-## Bias, Risks, and Limitations
+Next define a function that can help you with the prompt (alpaca style):
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```python
+def prompt(nlq, cols):
+    return f"""[INST] <<SYS>>
+Honeycomb AI suggests queries based on user input and candidate columns.
+<</SYS>>
 
-[More Information Needed]
+User Input: {nlq}
 
-### Recommendations
+Candidate Columns: {cols}
+[/INST]
+"""
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+def prompt_tok(nlq, cols):
+    _p = prompt(nlq, cols)
+    input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
+    out_ids = model.generate(input_ids=input_ids, max_new_tokens=5000, 
+                          do_sample=False)
+    return tokenizer.batch_decode(out_ids.detach().cpu().numpy(), 
+                                  skip_special_tokens=True)[0][len(_p):]
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Next, make predictions
 
-## How to Get Started with the Model
+```python
+nlq = "Exception count by exception and caller"
+cols = ['error', 'exception.message', 'exception.type', 'exception.stacktrace', 'SampleRate', 'name', 'db.user', 'type', 'duration_ms', 'db.name', 'service.name', 'http.method', 'db.system', 'status_code', 'db.operation', 'library.name', 'process.pid', 'net.transport', 'messaging.system', 'rpc.system', 'http.target', 'db.statement', 'library.version', 'status_message', 'parent_name', 'aws.region', 'process.command', 'rpc.method', 'span.kind', 'serializer.name', 'net.peer.name', 'rpc.service', 'http.scheme', 'process.runtime.name', 'serializer.format', 'serializer.renderer', 'net.peer.port', 'process.runtime.version', 'http.status_code', 'telemetry.sdk.language', 'trace.parent_id', 'process.runtime.description', 'span.num_events', 'messaging.destination', 'net.peer.ip', 'trace.trace_id', 'telemetry.instrumentation_library', 'trace.span_id', 'span.num_links', 'meta.signal_type', 'http.route']
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+out = prompt_tok(nlq, cols)
+print(out)
+```
 
 ## Training Details
 
-### Training Data
+See [this wandb run](https://wandb.ai/hamelsmu/hc-axolotl-mistral/runs/et2e62s4/overview?workspace=user-hamelsmu)
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+### Training Data
 
-[More Information Needed]
+~90k synthetically generated honeycomb queries. 
 
 ### Training Procedure 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Used [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main), see [this config](configs/config.yml).
 
 
 ## Training procedure